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Published Jan 31, 2025

Rodney Taw  

Abstract

Ants, albeit appearing diminutive and trivial within the broader context of nature, demonstrate an extraordinary capacity for collective problem-solving and exhibit intellect that beyond expectations for individual members of their species. This phenomenon, termed ants’ collective intelligence, has elicited attention and appreciation from both experts and enthusiasts. This article examines the complex mechanisms of ants’ collective intelligence, highlighting its essential features, ramifications across diverse domains, and the significant insights it can provide to human. Embark on an intriguing exploration of the ant kingdom, where collaboration, communication, and decentralized decision-making coalesce to create a complex system that offers significant lessons for human pursuits.

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Keywords

Ants Collective Intelligence, Decentralized Decision-Making, Collective Problem-Solving, Systematic Insights, Human Development

Supporting Agencies

No funding source declared.

References
Abbas, F., & Fan, P. (2018). Clustering-based reliable low-latency routing scheme using ACO method for vehicular networks. Vehicular Communications, 12, 66–74. DOI: https://doi.org/10.1016/j.vehcom.2018.02.004

Aldhuhoori, R., Almazrouei, K., Sakhrieh, A., Hazza, M. A., & Alnahhal, M. (2022). The effects of recruitment, selection, and training practices on employee performance in the construction and related industries. Civil Engineering Journal, 8(12), 3831–3841. DOI: https://doi.org/10.28991/cej-2022-08-12-012

Anderson, C., & Franks, N. R. (2003). Teamwork in animals, robots, and humans. In Advances in the study of behavior (pp. 1–48). DOI: https://doi.org/10.1016/s0065-3454(03)33001-3

Ant (Insecta, hymenoptera). (2005). Van Nostrand’s Scientific Encyclopedia. DOI: https://doi.org/10.1002/0471743984.vse0494
Arcaute, E., Christensen, K., Sendova-Franks, A., Dahl, T., Espinosa, A., & Jensen, H. J. (2008). Division of labour in ant colonies in terms of attractive fields. Ecological Complexity, 6(4), 396–402. DOI: https://doi.org/10.1016/j.ecocom.2008.10.001

Asadinia, S., Rafsanjani, N. M. K., & Saeid, N. a. B. (2010). A novel routing algorithm based-on ant colony in Mobile Ad hoc Networks. In 2010 3rd IEEE International Conference on Ubi-Media Computing (pp. 77–82). DOI: https://doi.org/10.1109/umedia.2010.5543922

Attygalle, A. B., & Morgan, E. D. (1985). Ant Trail Pheromones. In Advances in insect physiology (pp. 1–30). DOI: https://doi.org/10.1016/s0065-2806(08)60038-7

Berger, T., Sallez, Y., & Taho, C. (2006). Bio-Inspired Approach for Autonomous Routing in FMS. In Manufacturing the Future (pp. 101–124). DOI: https://doi.org/10.5772/5046

Bian, X., & Tian, J. (2022). The construction of intelligent supply chain system for agricultural products based on improved Ant colony algorithm. Mobile Information Systems, 2022, 1–13. DOI: https://doi.org/10.1155/2022/5643304

Bonabeau, E. (1998). Social insect colonies as complex adaptive systems. Ecosystems, 1(5), 437–443. DOI: https://doi.org/10.1007/s100219900038

Bonabeau, E., Dorigo, M., & Theraulaz, G. (2000). Inspiration for optimization from social insect behaviour. Nature, 406(6791), 39–42. DOI: https://doi.org/10.1038/35017500

Bonabeau, E., & Théraulaz, G. (2000). Swarm Smarts. Scientific American, 282(3), 72–79. DOI: https://doi.org/10.1038/scientificamerican0300-72

Cammaerts, M. (2012). The visual perception of the ant Myrmica ruginodis (Hymenoptera: Formicidae). Biologia, 67(6), 1165–1174. DOI: https://doi.org/10.2478/s11756-012-0112-z

Campos, M., Bonabeau, E., Théraulaz, G., & Deneubourg, J. (2000). Dynamic scheduling and division of labor in social insects. Adaptive Behavior, 8(2), 83–95. DOI: https://doi.org/10.1177/105971230000800201

Canciani, M., Arnellos, A., & Moreno, A. (2019). Revising the Superorganism: an organizational approach to complex eusociality. Frontiers in Psychology, 10. DOI: https://doi.org/10.3389/fpsyg.2019.02653

Chamanbaz, M., Mateo, D., Zoss, B. M., Tokić, G., Wilhelm, E., Bouffanais, R., & Yue, D. K. P. (2017). Swarm-Enabling technology for Multi-Robot systems. Frontiers in Robotics and AI, 4. DOI: https://doi.org/10.3389/frobt.2017.00012

Chapin, F. S., Mark, A. F., Mitchell, R. A., & Dickinson, K. J. M. (2012). Design principles for social‐ecological transformation toward sustainability: lessons from New Zealand sense of place. Ecosphere, 3(5), 1–22. DOI: https://doi.org/10.1890/es12-00009.1

Chopard, B., & Tomassini, M. (2018). The Ant Colony method. In Natural computing series (pp. 81–96). DOI: https://doi.org/10.1007/978-3-319-93073-2_5

Chowdhury, N. N. M., Baker, S. M., & Choudhury, E. H. (2008). A new adaptive routing approach based on Ant Colony Optimization (ACO) for Ad hoc Wireless Networks. In 2008 11th International Conference on Computer and Information Technology (Vol. 54, pp. 51–56). DOI: https://doi.org/10.1109/iccitechn.2008.4803126

Collignon, B., & Detrain, C. (2009). Distributed leadership and adaptive decision-making in the antTetramorium caespitum. Proceedings of the Royal Society B Biological Sciences, 277(1685), 1267–1273. DOI: https://doi.org/10.1098/rspb.2009.1976

Conway, J. R. (1986). The Biology of Honey ants. The American Biology Teacher, 48(6), 335–343. DOI: https://doi.org/10.2307/4448321

Czaczkes, T. J., Grüter, C., & Ratnieks, F. L. (2014). Trail Pheromones: An Integrative view of their role in social insect Colony organization. Annual Review of Entomology, 60(1), 581–599. DOI: https://doi.org/10.1146/annurev-ento-010814-020627

Dandan, N. S., & Hongxin, N. Z. (2014). A routing cooperative selection method based on ant colony optimization algorithm. In International Conference on Cyberspace Technology (CCT 2014) (p. 100 (4)). DOI: https://doi.org/10.1049/cp.2014.1363

Deeti, S., McLean, D. J., Murray, T., & Cheng, K. (2024). Route learning and transport of resources during colony relocation in Australian desert ants. Learning & Behavior. DOI: https://doi.org/10.3758/s13420-024-00652-1

Detrain, C., & Deneubourg, J. (2002). Complexity of environment and parsimony of decision rules in insect societies. Biological Bulletin, 202(3), 268–274. DOI: https://doi.org/10.2307/1543478

Diamé, L., Rey, J., Vayssières, J., Grechi, I., Chailleux, A., & Diarra, K. (2017). Ants: major functional elements in fruit Agro-Ecosystems and biological control agents. Sustainability, 10(1), 23. DOI: https://doi.org/10.3390/su10010023

Dorigo, M., Birattari, M., & Stutzle, T. (2007). Ant Colony Optimization. In Chapman and Hall/CRC eBooks (pp. 417–430). DOI: https://doi.org/10.1201/9781420010749-33

Dorigo, M., Bonabeau, E., & Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems, 16(8), 851–871. DOI: https://doi.org/10.1016/s0167-739x(00)00042-x

Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172. DOI: https://doi.org/10.1162/106454699568728

Dussutour, A., & Nicolis, S. C. (2013). Flexibility in collective decision-making by ant colonies: Tracking food across space and time. Chaos Solitons & Fractals, 50, 32–38. DOI: https://doi.org/10.1016/j.chaos.2013.02.004

Firthous, M. a. A., & Kumar, R. (2020). Multiple oriented robots for search and rescue operations. IOP Conference Series Materials Science and Engineering, 912(3), 032023. DOI: https://doi.org/10.1088/1757-899x/912/3/032023

Gao, Z., Guo, Q., & Wang, P. (2007). An adaptive routing based on an improved ant colony optimization in LEO satellite networks. International Conference on Machine Learning and Cybernetics, 1041–1044. DOI: https://doi.org/10.1109/icmlc.2007.4370296

Gibbons, J. W. (1990). Life history and ecology of the slider turtle. Choice Reviews Online, 28(04), 28–2131. DOI: https://doi.org/10.5860/choice.28-2131

Gligor, D., Gligor, N., Holcomb, M., & Bozkurt, S. (2019). Distinguishing between the concepts of supply chain agility and resilience. The International Journal of Logistics Management, 30(2), 467–487. DOI: https://doi.org/10.1108/ijlm-10-2017-0259

Gordon, D. M. (2007). Control without hierarchy. Nature, 446(7132), 143. DOI: https://doi.org/10.1038/446143a

Gordon, D. M. (2016). Collective wisdom of ants. Scientific American, 314(2), 44–47. DOI: https://doi.org/10.1038/scientificamerican0216-44

Habib, S. J., & Marimuthu, P. N. (2017). A bio-inspired tool for managing resilience in enterprise networks with embedded intelligent formulation. Expert Systems, 35(1). DOI: https://doi.org/10.1111/exsy.12208

Heflebower, R. B. (1960). Observations on decentralization in large enterprises. Journal of Industrial Economics, 9(1), 7. DOI: https://doi.org/10.2307/2097464

Heinze, J., Hölldobler, B., & Peeters, C. (1994). Conflict and cooperation in ant societies. The Science of Nature, 81(11), 489–497. DOI: https://doi.org/10.1007/bf01132680

Herbers, J. M. (1979). Caste-biased polyethism in a mound-building ant species. The American Midland Naturalist, 101(1), 69. DOI: https://doi.org/10.2307/2424902

Hickling, R., & Brown, R. L. (2000). Analysis of acoustic communication by ants. The Journal of the Acoustical Society of America, 108(4), 1920–1929. DOI: https://doi.org/10.1121/1.1290515

Hölldobler, B. (1978). Ethological aspects of chemical communication in ants. In Advances in the study of behavior (pp. 75–115). DOI: https://doi.org/10.1016/s0065-3454(08)60132-1

Ichimura, T., Uemoto, T., Hara, A., & Mackin, K. J. (2014). Emergence of altruism behavior in army ant-based social evolutionary system. SpringerPlus, 3(1). DOI: https://doi.org/10.1186/2193-1801-3-712

Jackson, D. E., & Ratnieks, F. L. (2006). Communication in ants. Current Biology, 16(15), R570–R574. DOI: https://doi.org/10.1016/j.cub.2006.07.015

Johnson, J. T. (2009). A Brief Investigation of Swarm Theory and Applications. In Volume 2: 29th Computers and Information in Engineering Conference (pp. 209–218). DOI: https://doi.org/10.1115/detc2009-86525

Karnish, A. (2024). Seed Dispersal by Ants: A primer. International Journal of Plant Sciences, 185(5), 403–411. DOI: https://doi.org/10.1086/730787

Kolay, S., Boulay, R., & D’Ettorre, P. (2020). Regulation of ant Foraging: A review of the role of information use and personality. Frontiers in Psychology, 11. DOI: https://doi.org/10.3389/fpsyg.2020.00734

Kube, C., & Bonabeau, E. (2000). Cooperative transport by ants and robots. Robotics and Autonomous Systems, 30(1–2), 85–101. DOI: https://doi.org/10.1016/s0921-8890(99)00066-4

Liao, R., Li, S., Wu, C., Zhang, X., Jiang, C., & Li, R. (2023). Modeling and simulation of extended ant colony labor division for benefit distribution of the all-for-one tourism supply chain with front and back decoupling. Frontiers in Bioengineering and Biotechnology, 11. DOI: https://doi.org/10.3389/fbioe.2023.985550

Mailleux, A., Detrain, C., & Deneubourg, J. (2006). Starvation drives a threshold triggering communication. Journal of Experimental Biology, 209(21), 4224–4229. DOI: https://doi.org/10.1242/jeb.02461

Middleton, E. J. T., & Latty, T. (2016). Resilience in social insect infrastructure systems. Journal of the Royal Society Interface, 13(116), 20151022. DOI: https://doi.org/10.1098/rsif.2015.1022

Milius, S. (2000). When ants squeak: Eavesdropping on lesser-known bulletins from the hill. Science News, 157(6), 92–94. DOI: https://doi.org/10.2307/4012245

Moffett, M. W. (2012). Supercolonies of billions in an invasive ant: What is a society? Behavioral Ecology, 23(5), 925–933. DOI: https://doi.org/10.1093/beheco/ars043

Moffett, M. W., Garnier, S., Eisenhardt, K. M., Furr, N. R., Warglien, M., Sartoris, C., Ocasio, W., Knudsen, T., Bach, L. A., & Offenberg, J. (2021). Ant colonies: building complex organizations with minuscule brains and no leaders. Journal of Organization Design, 10(1), 55–74. DOI: https://doi.org/10.1007/s41469-021-00093-4

Müller, M., & Wehner, R. (1988). Path integration in desert ants, Cataglyphis fortis. Proceedings of the National Academy of Sciences, 85(14), 5287–5290. DOI: https://doi.org/10.1073/pnas.85.14.5287

Muscedere, M. (2016). Pheidole Ants: Sociobiology of a highly diverse genus. In Elsevier eBooks (pp. 149–158). DOI: https://doi.org/10.1016/b978-0-12-809633-8.01208-5

Narendra, A., Ramirez-Esquivel, F., & Ribi, W. A. (2016). Compound eye and ocellar structure for walking and flying modes of locomotion in the Australian ant, Camponotus consobrinus. Scientific Reports, 6(1). DOI: https://doi.org/10.1038/srep22331

Narzt, W., Wilflingseder, U., Pomberger, G., Kolb, D., & HöRtner, H. (2010). Self-organising congestion evasion strategies using ant-based pheromones. IET Intelligent Transport Systems, 4(1), 93–102. DOI: https://doi.org/10.1049/iet-its.2009.0022

Navarro, I., & Matía, F. (2012). An introduction to swarm robotics. ISRN Robotics, 2013, 1–10. DOI: https://doi.org/10.5402/2013/608164

Oldham, N. J., Morgan, E. D., Gobin, B., & Billen, J. (1994). First identification of a trail pheromone of an army ant (Aenictus species). Experientia, 50(8), 763–765. DOI: https://doi.org/10.1007/bf01919378

Parker, C., & Zhang, N. H. (2009). Cooperative Decision-Making in decentralized Multiple-Robot Systems: The Best-of-N problem. IEEE/ASME Transactions on Mechatronics, 14(2), 240–251. DOI: https://doi.org/10.1109/tmech.2009.2014370

Parry, K., & Morgan, E. D. (1979). Pheromones of ants: a review. Physiological Entomology, 4(2), 161–189. DOI: https://doi.org/10.1111/j.1365-3032.1979.tb00193.x

Pavani, G. S., & Waldman, H. (2006). Traffic Engineering and Restoration in Optical Packet Switching Networks by means of Ant Colony Optimization. In 2006 3rd International Conference on Broadband Communications, Networks and Systems (pp. 1–10). DOI: https://doi.org/10.1109/broadnets.2006.4374329

Ramos, V., Fernandes, C., Rosa, A. C., & Abraham, A. (2007). Computational chemotaxis in ants and bacteria over dynamic environments. In 2007 IEEE Congress on Evolutionary Computation (pp. 1109–1117). DOI: https://doi.org/10.1109/cec.2007.4424594

Ramyar, R. (2017). Green infrastructure contribution for climate change adaptation in urban landscape context. Applied Ecology and Environmental Research, 15(3), 1193–1209. DOI: https://doi.org/10.15666/aeer/1503_11931209

Raouf, O. A., & Askr, H. (2019). ACOSDN-Ant Colony Optimization Algorithm for Dynamic Routing In Software Defined Networking. In 2019 14th International Conference on Computer Engineering and Systems (ICCES). DOI: https://doi.org/10.1109/icces48960.2019.9068162

Reid, C. R., Lutz, M. J., Powell, S., Kao, A. B., Couzin, I. D., & Garnier, S. (2015). Army ants dynamically adjust living bridges in response to a cost–benefit trade-off. Proceedings of the National Academy of Sciences, 112(49), 15113–15118. DOI: https://doi.org/10.1073/pnas.1512241112

Robinson, E. J. H., Green, K. E., Jenner, E. A., Holcombe, M., & Ratnieks, F. L. W. (2008). Decay rates of attractive and repellent pheromones in an ant foraging trail network. Insectes Sociaux, 55(3), 246–251. DOI: https://doi.org/10.1007/s00040-008-0994-5

Robinson, G. (1992). Regulation of division of labor in insect societies. Annual Review of Entomology, 37(1), 637–665. DOI: https://doi.org/10.1146/annurev.ento.37.1.637

Salman, M., Ramos, D. G., & Birattari, M. (2024). Automatic design of stigmergy-based behaviours for robot swarms. Communications Engineering, 3(1). DOI: https://doi.org/10.1038/s44172-024-00175-7

Satdeve, S. (2020). Human Resource Management: Service Sector vs Manufacturing Sector. Journal of Investment and Management, 9(4), 115. DOI: https://doi.org/10.11648/j.jim.20200904.14

Shields, V. D. (2018). Introductory chapter: The complex world of Ants. In InTech eBooks. DOI: https://doi.org/10.5772/intechopen.80387

Shik, J. Z., Santos, J. C., Seal, J. N., Kay, A., Mueller, U. G., & Kaspari, M. (2014). Metabolism and the rise of fungus cultivation by ants. The American Naturalist, 184(3), 364–373. DOI: https://doi.org/10.1086/677296

Sim, N. K. M., & Sun, N. W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Transactions on Systems Man and Cybernetics - Part a Systems and Humans, 33(5), 560–572. DOI: https://doi.org/10.1109/tsmca.2003.817391

Sim, Y., Lee, S., & Lee, S. (2017). Function-Oriented networking and On-Demand routing system in network using ANT Colony optimization algorithm. Symmetry, 9(11), 272. DOI: https://doi.org/10.3390/sym9110272

Smith, C. R., Dolezal, A., Eliyahu, D., Holbrook, C. T., & Gadau, J. (2009). Ants (Formicidae): Models for Social Complexity. Cold Spring Harbor Protocols, 2009(7), pdb.emo125. DOI: https://doi.org/10.1101/pdb.emo125

Stickland, T. R., Tofts, C., & Franks, N. R. (1993). Algorithms for ant foraging. The Science of Nature, 80(9), 427–430. DOI: https://doi.org/10.1007/bf01168341

Villet, M. H. (1990). Division of labour in the Matabele ant Megaponera foetens (Fabr.) (Hymenoptera Formicidae). Ethology Ecology & Evolution, 2(4), 397–417. DOI: https://doi.org/10.1080/08927014.1990.9525400

Wang, L., Tang, D., Yuan, W., Gu, W., & Tang, D. (2009). Pheromone-Based coordination and control for task allocation. International Conference on Information Engineering and Computer Science, 1–4. DOI: https://doi.org/10.1109/iciecs.2009.5365803

Wilson, E. O., & Hölldobler, B. (2005). Eusociality: Origin and consequences. Proceedings of the National Academy of Sciences, 102(38), 13367–13371. DOI: https://doi.org/10.1073/pnas.0505858102

Wilson, E. O., & Nowak, M. A. (2014). Natural selection drives the evolution of ant life cycles. Proceedings of the National Academy of Sciences, 111(35), 12585–12590. DOI: https://doi.org/10.1073/pnas.1405550111

Yogeswaran, M., & Ponnambalam, S. (2010). Swarm Robotics: An Extensive Research review. In Sciyo eBooks. DOI: https://doi.org/10.5772/10361

Yogeswaran, M., & Ponnambalam, S. G. (2009). An extensive review of research in swarm robotics. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). DOI: https://doi.org/10.1109/nabic.2009.5393617
How to Cite
Taw, R. (2025). Ants’ Collective Intelligence: What Could We Learn?. Science Insights, 46(1), 1711–1722. https://doi.org/10.15354/si.25.re1131
Section
Review